Learning first-order probabilistic models with combining rules

DOI10.1007/s10472-009-9138-5zbMath1178.68448OpenAlexW2065606385MaRDI QIDQ841645

Prasad Tadepalli, Thomas G. Dietterich, Sriraam Natarajan, Alan Fern

Publication date: 18 September 2009

Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10472-009-9138-5

zbMATH Keywords

predicate logicmulti-level combining rules

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Probability and inductive logic (03B48)

Related Items (8)

Fast learning of relational dependency networks ⋮ Exploiting symmetries for scaling loopy belief propagation and relational training ⋮ Learning Distributional Programs for Relational Autocompletion ⋮ Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases ⋮ Leveraging cluster backbones for improving MAP inference in statistical relational models ⋮ Gradient-based boosting for statistical relational learning: the relational dependency network case ⋮ Structure learning for relational logistic regression: an ensemble approach ⋮ Unnamed Item

Uses Software

• MEBN

Cites Work

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• Probabilistic Horn abduction and Bayesian networks
• Adaptive probabilistic networks with hidden variables
• MEBN: a language for first-order Bayesian knowledge bases
• PRL: a probabilistic relational language
• Efficient computation for the noisy MAX
• Inductive Logic Programming
• A Relational Hierarchical Model for Decision-Theoretic Assistance
• Noisy-or classifier
• The elements of statistical learning. Data mining, inference, and prediction

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